decideTests(limma)
decideTests()所属R语言包：limma
Multiple Testing Across Genes and Contrasts
在基因和对比多个测试
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Classify a series of related t-statistics as up, down or not significant. A number of different multiple testing schemes are offered which adjust for multiple testing down the genes as well as across contrasts for each gene.
分类的相关的t-统计的系列了，或不显着。提供一些不同的多个测试计划，调整下来的基因，每个基因的对比，以及跨多个测试。
用法----------Usage----------
decideTests(object,method="separate",adjust.method="BH",p.value=0.05,lfc=0)
参数----------Arguments----------
参数：object
MArrayLM object output from eBayes from which the t-statistics may be extracted.
MArrayLMeBayest-统计可提取的输出对象。“
参数：method
character string specify how probes and contrasts are to be combined in the multiple testing strategy. Choices are "separate", "global", "hierarchical", "nestedF" or any partial string.
字符串指定如何在多个测试策略相结合探针和对比。选择"separate"，"global"，"hierarchical"，"nestedF"或任何部分字符串。
参数：adjust.method
character string specifying p-value adjustment method. Possible values are "none", "BH", "fdr" (equivalent to "BH"), "BY" and "holm". See p.adjust for details.
字符串指定的P-值调整的方法。可能的值是"none"，"BH"，"fdr"（相当于"BH"）"BY"和"holm"。看到p.adjust详情。
参数：p.value
numeric value between 0 and 1 giving the desired size of the test
0和1之间的数值，测试所需的大小
参数：lfc
minimum log2-fold-change required
所需最低的log2倍变化
Details
详情----------Details----------
These functions implement multiple testing procedures for determining whether each statistic in a matrix of t-statistics should be considered significantly different from zero. Rows of tstat correspond to genes and columns to coefficients or contrasts.
这些功能实现多个测试程序，确定每一个矩阵t-统计量的统计是否应该被认为是显着异于零。 tstat行对应的基因和列系数或对比。
The setting method="separate" is equivalent to using topTable separately for each coefficient in the linear model fit, and will give the same lists of probes if adjust.method is the same. method="global" will treat the entire matrix of t-statistics as a single vector of unrelated tests. method="hierarchical" adjusts down genes and then across contrasts. method="nestedF" adjusts down genes and then uses classifyTestsF to classify contrasts as significant or not for the selected genes. Please see the limma User's Guide for a discussion of the statistical properties of these methods.
设置method="separate"相当于使用topTable分别为每线性模型的拟合系数，探针将给予同样的名单，如果adjust.method是相同的。 method="global"将把整个矩阵作为一个无关的测试向量t-统计。 method="hierarchical"调整下来的基因，然后跨对比。 method="nestedF"调整下来的基因，然后使用classifyTestsF分类对比显着或没有选定的基因。请参见的limma用户指南讨论了这些方法的统计性质。
值----------Value----------
An object of class TestResults. This is essentially a numeric matrix with elements -1, 0 or 1 depending on whether each t-statistic is classified as significantly negative, not significant or significantly positive respectively.
对象类TestResults。这实质上是一种数字矩阵元素-1，0或1取决于是否每个t-统计显着的负向，不显着或显着的正向分别归类。
If lfc>0 then contrasts are judged significant only when the log2-fold change is at least this large in absolute value. For example, one might choose lfc=log2(1.5) to restrict to 50% changes or lfc=1 for 2-fold changes. In this case, contrasts must satisfy both the p-value and the fold-change cutoff to be judged significant.
如果lfc>0然后对比判断，显著只log2倍的变化至少在绝对值大。例如，一个可能选择lfc=log2(1.5)限制到50％的变化或lfc=12倍的变化。在这种情况下，对比，必须满足两个p值和判断显着的变化倍截止。
作者（S）----------Author(s)----------
Gordon Smyth
参见----------See Also----------
An overview of multiple testing functions is given in 08.Tests.
多种测试功能概述在08.Tests给出。
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-2-25 23:13:35
